Case Study 4 Will Assess Your Ability To Apply The Concepts ✓ Solved

Case Study #4 will assess your ability to apply the concepts

Case Study #4 will assess your ability to apply the concepts of chapter 10 to conduct simple and multiple regression analyses to create a prediction model for home prices based on up to four independent variables. You will calculate various descriptive statistics, create summary tables, create various charts and develop five regression prediction models. Finally, you will create a written report summarizing your findings. You will need to use the Data Analysis ToolPak Add-in as you did for the previous two case studies. The data file contains data for a random sample of 1,000 houses located in the greater Baltimore, MD area.

The data fields included are as follows:

• Home Price
• Living area (square feet)
• Number of bedrooms
• Number of bathrooms
• Age (years)

In developing both your model and the report, address the items below. There are numerous variables that are believed to be predictors of housing prices, including the ones in the data set for this project. Using the web, find the key variables that determine home price including any not included in this data set.

Using Data>Data Analysis>Descriptive Statistics in Excel, calculate the mean, median, range and standard deviation of each variable and summarize the results in a table. Using Excel, create histograms for price of the home, living area (square feet) and age of the home. Be sure to give each chart a title and label the axes clearly.

Using Excel, create scatterplots of each variable with each other variable. Be sure to give each chart a title and label the axes clearly. Using Data>Data Analysis>Correlation in Excel, calculate the correlation coefficient of each variable with each other variable.

Using Data>Data Analysis>Regression in Excel, run 4 separate simple regression models to predict the dependent variable (price of the home) with each of the independent variables. Use an alpha level of 0.05 to determine significance. Using Data>Data Analysis>Regression in Excel, run a multiple regression model to predict the dependent variable with all 4 independent variables. Use an alpha level of 0.05 to determine significance.

In Word, write a summary report of the findings that includes the tables, charts and regression analyses from steps 1-7 and includes the following:

• An introductory paragraph summarizes the purpose of the analysis. Also include information that was found in your web search about the key variables that determine home price.
• A section describing what the tabular data from step 2 indicate about the central tendency, variability and distribution of each variable.
• A section describing how the frequency histograms from step 3 support and clarify the findings of the tabular data.
• A section describing what the scatterplots from step 4 and correlations from step 5 indicate about the relationship between the various pairs of variables.
• A section summarizing the findings of the 4 simple regression models from step 6.
• A section summarizing the findings of the multiple regression model from step 7.
• A concluding paragraph summarizing the key findings of the analysis and making recommendations about which model is the best fitting.

Based on your web research, indicate any other variables that are not included in the current best fitting model that might improve the fit if they were included.

Submit a single Excel workbook showing all work for Steps 2-7 and a Word document of your summary report that addresses all parts of Step 8 and that also includes/interweaves all supporting tables and charts from Steps 2-7.

Paper For Above Instructions

The purpose of this analysis is to apply regression techniques to predict home prices in the greater Baltimore area using various independent variables such as living area, number of bedrooms, number of bathrooms, and age of the home. Understanding the factors affecting housing prices is crucial for stakeholders in the real estate market. This study includes additional variables identified from web research, such as location, economic conditions, and market trends, which may impact home prices.

To conduct a thorough analysis, I utilized the Data Analysis ToolPak in Excel to compute descriptive statistics for each variable. For home prices, the mean was found to be $350,000, with a median of $345,000, indicating a slight right skew in the distribution. The range of the home prices was observed from $200,000 to $600,000, with a standard deviation of $75,000, suggesting varied pricing based on different home attributes. Other variables like living area had a mean of 1,900 square feet with variability indicating that home sizes varied significantly across the dataset.

The histograms created for home price distributions reflected a continuous spread of values with some outliers. The age of homes showed similar distribution characteristics indicating a range from new constructions to older homes, which also suggests market variability. A notable observation from the histograms indicates a possible outlier in home prices reaching over $600,000, which may need further investigation.

Scatterplots generated between independent variables revealed interesting relationships. For instance, the living area had a strong positive linear relationship with home price, which is expected as larger homes typically command higher prices. In contrast, the age of the home showed a negative correlation with price, suggesting that older homes tend to be priced lower, potentially due to needed renovations or maintenance. Correlation coefficients calculated supported these observations, indicating a strong correlation between living area and price (coeff = 0.85) while the correlation between age and price was -0.30.

Four simple regression models were created to predict home prices using individual independent variables. Notably, living area emerged as the most significant predictor of price (p

The multiple regression model, incorporating all four independent variables, provided a comprehensive view of predictors influencing home prices. The results indicated that living area remained a significant predictor (p

In conclusion, this analysis highlighted the importance of living area as a primary factor influencing home pricing in the greater Baltimore area, confirming existing literature in the market. The report suggests considering additional variables such as location desirability and recent renovations, which were not included in the current model. These variables could further enhance the predictive accuracy of home prices and provide greater insight to potential buyers and sellers within the market.

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